With the development and implementation of High-Throughput Systems (HTS) the pharmaceutical industry faces more and more the problem of an adequate analysis of the obtained data. In particular, there is a need for conversion of information derived from multi-dimensional data matrixes into structural chemical information.
It is the object of the present invention to provide a method for automatic finding of potential correlations in complex data matrixes. It is a further object of the present invention to provide a user with chemical structural or chemical property information about the latent multidimensional cohesions. These objects are achieved with the features of the claims.
According to a first aspect, the present invention provides a method directed to the analysis of the effect of a structural fragment of a chemical structure on at least one biological descriptor.
The method according to the first aspect comprises the steps of:                A) providing data about at least one set of chemical structures;        B) providing data about at least one biological descriptor;        C) calculating structural fragments for each chemical structure;        D) associating said structural fragments for each chemical structure with said biological descriptor data in order to obtain a data matrix;        E) analysing the data in said data matrix with a view on the effect of a fragment of each chemical structure on said biological descriptor; and        F) visualizing the result of said analysis.        
Preferably, in step e), each of the fragments calculated in step c) is linked to the biological descriptor data. This is preferably done using a partial-least-square method. Alternatively, neural networks, multiple linear regression, or support vector machines are used.
In step d) of the method of the first aspect of the present invention, the fragments are preferably selected on the basis of a predefined scaffold structure. In this context, the term “predefined scaffold structure” means a basic structure being selected by the user of the method, for example a chemist, and the fragments of such basic structure are varied. Predefined scaffolds structures can also be identified by an automated search for the most common chemical substructure. As an alternative, the present method encompasses that in step d) the fragments are generated on the basis of molecular fragments.
According to a preferred embodiment of the method according to the first aspect, step d) is followed by the step of calculating physico-chemical properties for each fragment. Preferably, step e) analyses the data in the data matrix with a view on the effect of a fragment and its physico-chemical properties on the biological descriptor. Each physico-chemical property of each fragment is preferably linked to the biological descriptor data.
According to a second aspect, the present invention provides a method directed to the analysis of the effect of physico-chemical properties of a chemical structure on at least one biological descriptor.
The method according to the second aspect comprises the steps of:                A) providing data about at least one set of chemical structures;        B) providing data about one biological descriptor;        C) calculating physico-chemical properties of each chemical structure;        D) associating said physico-chemical properties of each chemical structure with said biological descriptor data in order to obtain a data matrix;        E) analysing the data in said data matrix with a view on the effect of each physico-chemical property of each chemical structure on said biological descriptor; and        F) visualizing the result of said analysis.        
Preferably, in step E), each physico-chemical property of each chemical structure is linked to said biological descriptor data.
The physico-chemical properties of said chemical structures or fragments are preferably selected from the group comprising hydrophilicity, hydrophobicity, electricity, shape, and amphiphilicity. However, the present invention is not limited to these properties. In this context, reference is made to Todeschini, Roberto Consonni, Viviana, Handbook of Molecular Descriptors, Methods and Principles in Medical Chemistry Vol. 11, WILEY-VCH, 2000.
According to the methods of the first and second aspect, the biological descriptor data are independently selected from pharmacological, toxicological, and pharmacokinetic data. For example, the at least one biological descriptor is selected from the group comprising descriptors relating to receptor activity, selectivity, bio-availability, and in vivo activity. However, these biological descriptors are only examples of a variety of descriptors that are preferably used in the present invention. Further examples of potential descriptors are given below.
Like in the method of the first aspect, the second aspect of the present invention preferably uses a partial-least-square method for analysing the data in the data matrix with a view on the effect of each physico-chemical property of each chemical structure on the biological descriptor (step E)). Alternatively, as mentioned above, neural networks, multiple linear regression, or support vector machines are used.
In the following, preferred features of the first and the second aspect are described.
Preferably, the visualisation of the results is made as a two-dimensional pattern or matrix dependent on the chemical structure and the biological descriptor(s), wherein the pattern represents a positive or negative effect of each of the fragments or each physico-chemical property on the biological descriptor(s). More preferably, the portions of the pattern representing a positive effect of each of the fragments or each physico-chemical property on the biological descriptor are highlighted different from the portions of the pattern representing a negative effect of each of the fragments or each physico-chemical property on the biological descriptor(s). Most preferably, the different portions are coloured differently.
The methods of the first or second aspect may further comprise the step of validating the result with an independent data set.
According to a further preferred embodiment, the methods comprise the step of automatically generating new molecules with favourable multi-dimensional profiles.
In the following, the method steps of the present invention are described in more detail. The methods of the present invention start with the data input (chemical structures and one or more biological descriptors with its activities). Preferably, at least three chemical structures, more preferably five, are provided as input data. Furthermore, the biological parameter or descriptor is provided as input data, and the activities of this descriptor. Preferably three activities are input, more preferably five activities for each biological descriptor.
Based on these input data, according to the method of the first aspect of the present invention, the fragments of the chemical structures are varied in order to obtain various sets for the data analysis, and the PLS is performed on these various data sets. As an example, the following equation is used according to the present invention:Y=a·FragmentA+b·FragmentB+ . . . +x·FragmentN+Z  (1)
wherein Y represents the activity value of the biological descriptor being provided as input, FragmentA through FragmentN represent the fragments that are considered, a through x are coefficients, and z is a constant. Each parameter FragmentI is either 1 or 0 dependent on whether the fragment is considered in the particular model or not.
This general formula shows that in principle a plurality of fragments can be considered for the data analysis. However, in practice, it is desirable to have only a few number of fragments in order to keep the number of necessary calculations at a reasonable minimum, and more importantly to ensure that the results can be interpreted and that the influence and effect of each fragment can be determined by the user.
According to the method of the second aspect of the present invention, again based on these input data, the physico-chemical properties of the chemical structures are for example calculated or read from a database, and the PLS is performed on these various models. As an example, the following formula is used according to the present invention:Y=a·property1+b·property2+ . . . +x·propertyN+z  (2)
wherein Y represents the activity value of the biological descriptor being provided as input, property1 through propertyN represent the physico-chemical properties that are considered, a through x are coefficients, and z is a constant. This general formula shows that in principle a plurality of properties can be considered for the data analysis. However, in practice, it is desirable to have only a few number of properties in order to keep the number of necessary calculations at a reasonable minimum, and more importantly to ensure that the results can be interpreted and that the influence and effect of each property can be determined.
Typically, equations (1) and (2) are solved for all activities provided as input data, i.e. the unknown coefficients and constant of these equations are calculated on the basis of the available equations, one for each activity.
Preferably, the coefficients are normalized and centralized so that they are comparable with each other (because they may have different dimensions).
According to the present invention, three aspects are an indication for the quality of the model (i.e. three quality parameters). First, the variable importance, i.e., the relevance of the fragment or physico-chemical property. Secondly, the absolute value of the calculated coefficients (in case PLS is used for the data analysis). If a calculated coefficient (of above equations (1) or (2)) is small, the effect of the associated variable is only small, even if this variable is an important one. Thirdly, the number of variables (fragments or physico-chemical properties) that are used. Basically, as mentioned above, the number of variables should be as small as possible in order to have a model that is interpretable by the user.